Dimensionality reduction of hyperspectral images with wavelet based Empirical Mode Decomposition

This paper presents an application of the Empirical Mode Decomposition (EMD) method to wavelet based dimensionality reduction, with an aim to generate the smallest set of features that leads to the best classification accuracy. Useful spectral information for hyper-spectral image (HSI) classification can be obtained by applying the Wavelet Transform (WT) to each hyperspectral signature. As EMD has the ability to describe short term spatial changes in frequencies, it helps to get a better understanding of the spatial information of the signal. In order to take advantage of both spectral and spatial information, a novel dimensionality reduction method is introduced, which relies on using the wavelet transform of EMD features. This leads to better class separability and hence to better classification. Specifically, the 2D-EMD is applied to each hyperspectral band and the 1D-DWT is applied to each EMD feature of all bands in order to get reduced Wavelet-based Intrinsic Mode Function Features (WIMF). Then, new features are generated by summing up the lower order WIMF features. The superiority of the proposed method compared to direct wavelet-based dimensionality reduction methods is proven by using the AVIRIS Indian Pine hyperspectral data. Compared to conventional direct wavelet-based dimensionality reduction methods, our proposed method offers up to 65% dimensionality reduction for the same classification performance.